Tabla de Contenidos: “…Avant-propos -- Table des matiÃ?res -- Introduction -- A. Remarques historiques -- B. …”

“…This Special Issue will investigate the most recent developments and research on solar power systems. …”

“…Additionally, you'll learn and explore the most useful built-in variables, pragmas, and modules available in the Perl language. …”

“…You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. …”

“…During the most recent conference of the Renaissance Society of America, two sessions were devoted entirely to the Renaissance in Poland. …”

Tabla de Contenidos: “…SALUSTIANO DE OLÓZAGA: LA RES MÁS BRAVA DEL PROGRESISMOUn progresista bajo sospecha; El gusto de la libertad; Entre el orden y la revolución: un puente hacia el Parlamento; La hora de la política y de la palabra; El progreso como transacción y la transacción dentro del progreso; De la libertad y del talento: un horizonte abierto de derechos; La monarquía representativa del progreso; De un golpe de Estado a un golpe de nación y a una Salve; El «incidente Olózaga» como símbolo de un reinado; El capitán de los muertos y la penúltima des-ilusión monárquica del Partido Progresista…”

Tabla de Contenidos: “…-- Understanding Curiosity -- Children, Curiosity, and Uncertainty -- Environments Conducive to Curiosity -- Unleashing Curiosity -- Flourishing in the Flow of Ideas -- Einstein the Patent Clerk -- Time and Space for J.S. Bach -- Deep Work to Make a Great Coffee -- Audacious Questions -- ResMed's Big Questions -- Killer Questions at Space X -- Novelty, Gestation, and Safety -- Novelty and Surprise -- Quiescent Curiosity -- Safety and Creativity -- Building Curiosity in Your Organization -- Chapter 2 Dragonfly Eye -- A New Lens on Recidivism -- Seeing Like a Dragonfly -- Anchoring Outside -- Framing the Dragonfly Eye -- The Power of Changing Lenses -- Beer Kegs as Technology -- A Fresh Look at Braces -- Choosing the Wrong Lens: WeWork -- Widening the Aperture -- Amazon's Head Start in Cloud Computing -- Solving a Health Crisis -- A Multi-Lens Approach -- The Neurodiversity Lens and a Digital Twin for Kelp Forests -- Obesity and Wicked Problems -- Bringing a Dragonfly Eye into Your Organization -- Chapter 3 Occurrent Behavior -- Let's Weigh the Money -- Occurrent Behavior and Problem Solving -- Thinking Like a Bayesian -- Framing Occurrent Behavior -- Experiment to Create New Data -- Autonomous Vehicle Experiments -- Space X: Relentless Experimenting -- A/B Testing at Airtasker -- Education Perfect: What the Educators Learned -- New Tools and Approaches…”

“…On peut alors s’interroger sur le droit qui guide les actions d’Yvain : est-ce celui du plus juste, ou du plus fort ?…”

“…Ses recherches l'ont ainsi conduit, au-delà de la linguistique, vers l’ethnolinguistique et plus particulièrement l'ethnolittérature, mais aussi l’onomastique, l’édition et l’étude de textes occitans non littéraires du Moyen Age, la littérature occitane et française, la poétique enfin. …”

Tabla de Contenidos: “…El capitalisme social i el món del treball; 4.2.1. Visibilitzar tots els actors implicats en una xarxa; 4.2.2. Remuneracions més justes; 4.2.3. …”

“…As a result, view-based 3-D object retrieval has become an essential but challenging res…”

Tabla de Contenidos: “…Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model…”